
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))


# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "54"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder


# Use environment variables to figure out path to the csvs and auto_metrics
antenna = os.environ["ANTENNA"]
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')

antenna = "142"
csv_folder = "/home/obs/src/H5C_Notebooks/_rtp_summary_"
auto_metrics_folder = "/home/obs/src/H5C_Notebooks/auto_metrics_inspect"


display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))


import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna


# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, '*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')

Found 106 csvs in /home/obs/src/H5C_Notebooks/_rtp_summary_
Found 102 auto_metrics notebooks in /home/obs/src/H5C_Notebooks/auto_metrics_inspect


# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0

def jd_to_summary_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'

def jd_to_auto_metrics_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'


this_antenna = None
jds = []

# parse information about antennas and nodes
for csv in csvs:
    df = pd.read_csv(csv)
    for n in range(len(df)):
        # Add this day to the antenna
        row = df.loc[n]
        antnum = row['Ant']
        if antnum != int(antenna):
            continue
            
        if this_antenna is None:
            this_antenna = Antenna(row['Ant'], row['Node'])
        jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
        jds.append(jd)
        this_antenna.add_day(jd, row)
        break


# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]

df = pd.DataFrame(to_show)

# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
    df[col] = bar_cols[col]

z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
    df[col] = z_score_cols[col]

ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
    df[col] = ant_metrics_cols[col]

redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]   
for col in redcal_cols:
    df[col] = redcal_cols[col]

# style dataframe
table = df.style.hide_index()\
          .applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
          .background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
          .background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
          .applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
          .format({col: '{:,.4f}'.format for col in z_score_cols}) \
          .format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
          .format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
          .set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])


display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))


html_to_display = ''
for am_html in auto_metric_htmls:
    # read html into a list of lines
    with open(am_html) as f:
        lines = f.readlines()
    
    # find section with this antenna's metric plots and add to html_to_display
    jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
    try:
        section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
    except ValueError:
        continue
    html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
    for line in lines[section_start_line + 1:]:
        html_to_display += line
        if '<hr' in line: 
            break


HTML(html_to_display)

JDs	A Priori Status	Auto Metrics Flags	Dead Fraction in Ant Metrics (Jee)	Dead Fraction in Ant Metrics (Jnn)	Crossed Fraction in Ant Metrics	Flag Fraction Before Redcal	Flagged By Redcal chi^2 Fraction	ee Shape Modified Z-Score	nn Shape Modified Z-Score	ee Power Modified Z-Score	nn Power Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Discontinuties Modified Z-Score	nn Temporal Discontinuties Modified Z-Score	Average Dead Ant Metric (Jee)	Average Dead Ant Metric (Jnn)	Average Crossed Ant Metric	Median chi^2 Per Antenna (Jee)	Median chi^2 Per Antenna (Jnn)
2459594	RF_maintenance	100.00%	0.00%	100.00%	0.00%	100.00%	0.00%	1.584434	12.004407	-0.364365	80.921375	-0.106743	26.884570	-0.352275	31.208944	0.8022	0.0675	0.4805	13.917269	1.465581
2459593	RF_maintenance	100.00%	0.00%	100.00%	0.00%	100.00%	0.00%	2.871190	18.969505	-0.009053	85.510581	0.666825	37.855968	0.623695	11.201099	0.7515	0.0644	0.4812	5.467202	1.548135
2459592	RF_maintenance	100.00%	100.00%	100.00%	0.00%	-	-	0.524405	1.435516	-0.115182	7.271812	-1.000880	1.376950	-1.797130	0.106591	0.0468	0.0350	0.0048	nan	nan
2459591	RF_maintenance	100.00%	0.00%	100.00%	0.00%	100.00%	0.00%	2.597027	21.257501	-0.244292	99.409130	-0.078087	32.516011	-0.803747	1.240771	0.6914	0.0642	0.4527	4.403932	1.354264
2459590	RF_maintenance	100.00%	0.00%	100.00%	0.00%	100.00%	0.00%	3.767015	24.914637	-0.412196	92.611398	0.041182	34.956950	-0.534385	4.163995	0.6896	0.0652	0.4485	5.356468	1.432254
2459589	RF_maintenance	100.00%	0.00%	100.00%	0.00%	100.00%	0.00%	2.389536	22.244916	-0.734067	85.887549	0.826567	21.959266	0.364677	16.743369	0.7307	0.0685	0.4469	7.786404	1.668793
2459588	RF_maintenance	100.00%	0.00%	100.00%	0.00%	100.00%	0.00%	2.513587	16.157678	0.311099	105.099246	0.614660	25.114622	0.516859	13.188414	0.7940	0.0698	0.4791	7.986373	1.329524
2459587	RF_maintenance	100.00%	-	-	-	100.00%	0.00%	354.833874	354.669158	inf	inf	103.104237	103.251810	581.807603	604.428920	nan	nan	nan	0.000000	0.000000
2459586	RF_maintenance	100.00%	0.00%	100.00%	0.00%	100.00%	0.00%	0.494387	20.415488	-0.821140	32.418284	-0.141502	11.593565	0.260444	6.165023	0.6732	0.0637	0.4612	4.272708	1.430605
2459585	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.015955	24.812417	-1.085038	10.942967	-0.786549	10.189957	-1.044232	3.091656	0.0421	0.0402	0.0075	0.000000	0.000000
2459584	RF_maintenance	100.00%	0.00%	100.00%	0.00%	100.00%	0.00%	4.197501	28.016892	4.516809	115.688741	1.605739	18.174710	7.522868	13.865156	0.6311	0.0491	0.4223	0.000000	0.000000
2459583	RF_maintenance	100.00%	0.00%	100.00%	0.00%	100.00%	0.00%	1.011532	20.808876	0.849913	35.679682	-0.217228	23.647529	-0.059111	0.188735	0.6920	0.0614	0.4612	8.536731	1.080544
2459582	RF_maintenance	100.00%	0.00%	100.00%	0.00%	100.00%	0.00%	1.546261	17.710645	1.000157	32.417146	-0.048704	28.531760	-0.582307	0.333511	0.6933	0.0552	0.4727	8.542959	1.539678
2459581	RF_maintenance	100.00%	0.00%	100.00%	0.00%	100.00%	0.00%	2.812503	24.429426	0.282508	36.592188	-0.359712	23.030271	0.823352	0.363735	0.7011	0.0625	0.4608	4.279544	1.566272
2459580	RF_maintenance	100.00%	0.00%	100.00%	0.00%	100.00%	0.00%	2.543527	18.675537	1.120953	37.470799	0.087036	24.304502	3.149362	0.129260	0.6919	0.0624	0.4674	0.000000	0.000000
2459579	RF_maintenance	100.00%	0.00%	100.00%	0.00%	100.00%	0.00%	1.476093	18.462710	1.294712	42.576614	-0.086551	27.507591	-0.376017	1.489072	0.6907	0.0583	0.4586	5.100183	1.697419
2459578	RF_maintenance	0.00%	100.00%	100.00%	0.00%	-	-	0.100105	1.450238	-0.480412	2.755490	1.916114	0.366911	0.893003	0.857192	0.0546	0.0414	0.0132	nan	nan
2459577	RF_maintenance	100.00%	0.00%	100.00%	0.00%	100.00%	0.00%	2.849527	23.510125	0.274621	34.215985	-0.238780	24.910837	0.506675	0.382251	0.6878	0.0575	0.4835	5.085204	1.613590
2459576	RF_maintenance	100.00%	0.00%	100.00%	0.00%	100.00%	0.00%	1.583618	18.033739	0.052343	27.453565	0.105273	21.955153	-0.022434	1.049350	0.7149	0.0593	0.4391	5.729705	1.649861
2459575	RF_maintenance	100.00%	0.00%	100.00%	0.00%	100.00%	0.00%	0.968912	18.397227	0.210099	27.254497	-0.563970	17.096090	0.226092	5.987018	0.7710	0.0720	0.4659	11.360897	1.454820
2459574	RF_maintenance	100.00%	0.00%	100.00%	0.00%	100.00%	0.00%	1.648008	17.231175	-0.109378	26.083438	0.353420	24.317958	0.132835	0.636654	0.7085	0.0558	0.4181	6.766059	1.616288
2459573	RF_maintenance	100.00%	0.00%	100.00%	0.00%	100.00%	0.00%	1.757615	17.914217	1.387297	44.847683	-0.079555	28.478946	-0.124969	1.328604	0.7128	0.0579	0.4328	5.216162	1.422073
2459572	RF_maintenance	100.00%	0.00%	100.00%	0.00%	100.00%	0.00%	1.600725	17.929189	0.219002	26.236463	-0.178893	18.095779	-0.650303	2.474579	0.7138	0.0620	0.4346	4.438100	1.622672
2459571	RF_maintenance	0.00%	100.00%	100.00%	0.00%	-	-	0.273669	1.151395	-0.412697	2.842429	-0.131659	0.200429	0.528717	0.986178	0.0362	0.0318	0.0030	nan	nan
2459570	RF_maintenance	100.00%	0.00%	100.00%	0.00%	100.00%	0.00%	-0.386575	21.792423	-0.413679	27.611392	0.157554	1.797524	0.307577	3.186149	0.7116	0.0825	0.4338	0.000000	0.000000
2459569	RF_maintenance	100.00%	0.00%	100.00%	0.00%	100.00%	0.00%	1.281161	21.410918	0.025040	34.044521	0.055407	10.567704	-0.009391	8.742257	0.7850	0.0950	0.4261	5.561722	1.568439
2459566	RF_maintenance	100.00%	0.00%	100.00%	0.00%	100.00%	0.00%	1.679543	17.212566	0.425462	34.015599	0.066878	21.776602	0.278198	-0.192196	0.7052	0.0503	0.4026	5.160719	1.652227
2459565	RF_maintenance	0.00%	-	-	-	100.00%	0.00%	0.176781	0.176781	0.138346	0.138346	0.470698	0.470698	0.005728	0.005728	nan	nan	nan	0.000000	0.000000
2459564	RF_maintenance	100.00%	0.00%	100.00%	0.00%	100.00%	0.00%	0.725437	16.412175	-0.494374	36.138318	1.495013	2.391671	1.541362	0.880594	0.7250	0.0622	0.4692	4.884773	1.351568
2459563	RF_maintenance	100.00%	0.00%	100.00%	0.00%	100.00%	0.00%	2.912216	18.358507	-0.327154	68.938114	0.199665	22.468486	2.255688	1.474472	0.7186	0.0598	0.4674	4.229436	1.526913
2459562	RF_maintenance	100.00%	0.00%	100.00%	0.00%	100.00%	0.00%	0.326164	11.490464	-1.009106	17.780492	-0.098000	2.752962	0.953690	0.646072	0.7216	0.0606	0.4737	3.423379	1.447610
2459561	RF_maintenance	100.00%	0.00%	100.00%	0.00%	100.00%	0.00%	0.555457	12.325141	-1.154459	16.935304	-0.487477	3.015626	0.395763	0.955256	0.7190	0.0635	0.4701	3.092289	1.445723
2459560	RF_maintenance	100.00%	0.00%	100.00%	0.00%	100.00%	0.00%	-0.179070	11.236201	-0.889425	17.175303	0.099973	3.106456	1.467096	0.445017	0.7168	0.0597	0.4690	3.493664	1.457883
2459559	RF_maintenance	100.00%	0.00%	100.00%	0.00%	100.00%	0.00%	0.929352	14.236437	-0.940875	18.855436	-0.192686	3.427429	0.052754	0.814916	0.7147	0.0638	0.4624	2.991263	1.406731
2459558	RF_maintenance	100.00%	0.00%	100.00%	0.00%	100.00%	0.00%	6.561077	18.528264	0.650410	36.048135	0.306861	23.468631	0.081511	1.167733	0.7184	0.0606	0.4597	4.131765	1.542961
2459557	RF_maintenance	-	100.00%	100.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0585	0.0421	0.0094	nan	nan
2459556	RF_maintenance	100.00%	0.00%	100.00%	0.00%	100.00%	0.00%	8.525339	19.042186	0.504617	39.028608	-0.000909	21.128532	-0.267036	2.912028	0.7105	0.0621	0.4620	3.605817	1.477127
2459554	RF_maintenance	-	0.00%	100.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.7140	0.0593	0.4588	nan	nan
2459553	RF_maintenance	-	0.00%	100.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.7095	0.0637	0.4650	nan	nan
2459552	RF_maintenance	100.00%	0.00%	100.00%	0.00%	100.00%	0.00%	2.946160	22.616178	1.448067	51.482157	2.687886	32.095581	0.135293	2.872528	0.7254	0.0557	0.4695	7.909229	1.520904
2459551	RF_maintenance	100.00%	0.00%	100.00%	0.00%	100.00%	0.00%	-0.297799	11.054487	-1.281249	20.899833	-0.628915	4.246308	0.771096	0.971409	0.7222	0.0601	0.4666	3.455488	1.455664
2459550	RF_maintenance	-	97.53%	97.53%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0528	0.0463	0.0025	nan	nan
2459549	RF_maintenance	100.00%	0.00%	100.00%	0.00%	100.00%	0.00%	1.853238	16.728515	0.770554	38.893660	-0.213377	23.077303	0.921237	1.011562	0.7180	0.0553	0.4701	3.772213	1.549852
2459542	RF_maintenance	100.00%	0.00%	100.00%	0.00%	100.00%	0.00%	2.170954	24.111404	-0.730911	78.768010	1.460364	1.911705	0.542066	1.031210	0.7623	0.0583	0.4800	3.900455	1.454942
2459541	RF_maintenance	100.00%	1.34%	100.00%	0.00%	100.00%	0.00%	2.115191	11.110811	1.162227	39.319868	0.127318	241.520741	1.157569	7.570882	0.7585	0.0668	0.4597	4.904677	1.384662
2459540	RF_maintenance	100.00%	0.00%	100.00%	0.00%	100.00%	0.00%	0.891716	16.815781	-1.399834	17.929044	-0.811646	5.202255	0.079785	0.787536	0.7651	0.0602	0.4700	3.534258	1.464550
2459536	RF_maintenance	100.00%	0.00%	100.00%	0.00%	100.00%	0.00%	2.352813	27.758400	1.580882	66.936185	0.880509	31.682870	0.235568	1.882418	0.7494	0.0539	0.4257	6.774670	1.504553
2459535	RF_maintenance	100.00%	0.00%	100.00%	0.00%	100.00%	0.00%	0.099100	13.543359	-1.137524	16.461901	-0.485044	5.425281	0.266981	0.214437	0.7972	0.0666	0.4229	3.438862	1.497625
2459534	RF_maintenance	100.00%	0.00%	100.00%	0.00%	100.00%	0.00%	-0.236533	13.843582	-1.133760	15.229484	0.100828	3.806755	1.240263	-0.087817	0.7816	0.0551	0.3411	3.923602	1.514381
2459533	RF_maintenance	100.00%	0.00%	100.00%	0.00%	100.00%	0.00%	1.871039	20.562496	1.200974	36.518232	-0.018989	20.803189	1.205572	1.336863	0.7370	0.0516	0.3852	4.052281	1.342545
2459532	RF_maintenance	100.00%	0.00%	100.00%	0.00%	100.00%	0.00%	2.431494	23.754733	1.566612	49.485763	0.142409	25.517806	0.897282	0.705162	0.7430	0.0548	0.3900	4.929870	1.455168
2459530	RF_maintenance	100.00%	0.00%	100.00%	0.00%	100.00%	0.00%	-0.219818	12.070583	-0.998183	15.462283	-0.762752	4.276366	1.785364	0.369591	0.7578	0.0632	0.3725	3.607997	1.600618
2459527	RF_maintenance	0.00%	-	-	-	100.00%	0.00%	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459524	RF_maintenance	100.00%	0.00%	100.00%	0.00%	100.00%	0.00%	3.475919	28.600872	1.391072	55.141030	0.976914	34.926500	2.201850	0.415078	0.9144	0.0749	0.5292	0.000000	0.000000
2459523	RF_maintenance	100.00%	-	-	-	100.00%	0.00%	3.965170	23.738669	0.822906	52.704407	1.152842	29.423552	3.412784	1.981408	nan	nan	nan	6.668492	1.711328
2459522	RF_maintenance	100.00%	0.00%	100.00%	0.00%	100.00%	0.00%	3.592354	24.641000	1.065217	53.503353	1.263296	23.507322	2.424047	1.555389	0.7208	0.0581	0.4629	5.335772	1.409794
2459513	RF_maintenance	0.00%	100.00%	100.00%	0.00%	-	-	0.436035	1.587422	-0.584509	3.067264	1.971396	1.235107	0.884000	0.472760	0.0685	0.0693	0.0072	nan	nan
2459508	RF_maintenance	100.00%	0.00%	100.00%	0.00%	100.00%	0.00%	1.800519	31.620055	0.952858	51.949728	-0.176124	15.597927	0.868726	14.590339	0.9223	0.0849	0.5315	0.000000	0.000000
2459505	RF_maintenance	100.00%	0.00%	100.00%	0.00%	100.00%	0.00%	2.348397	30.676442	1.248494	50.266564	1.391155	10.120297	1.169630	1.625853	0.8852	0.0632	0.6203	17.785488	1.947196
2459503	RF_maintenance	100.00%	0.00%	100.00%	0.00%	100.00%	0.00%	3.860450	30.382259	1.792789	84.948596	1.799519	33.299241	2.307697	2.788898	0.8906	0.0635	0.6893	0.000000	0.000000

Antenna Season Report Notebook¶

Antenna 142 Report

Load relevant info from summary CSVs¶

Table 1: Per-Night RTP Summary Info For This Atenna¶

Antenna 142, Node 13:

Load antenna metric spectra and waterfalls from auto_metrics notebooks.¶

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

Antenna 142: 2459630

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Load antenna metric spectra and waterfalls from `auto_metrics` notebooks.¶